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We have previously identified 30 differentially expressed genes when comparing recently diagnosed type 1 diabetes mellitus (DM-1) patients and controls paired for sex, age, and ethnic background. In this article we performed the hierarchical clustering of these genes taking into account the human-leukocyte-antigen (HLA)-DRB1/DQB1 profile. The dendrogram obtained using the Cluster program grouped patients and controls into three clusters, one including individuals with no susceptibility alleles, another including individuals with at least three susceptibility alleles, and a third intermingling susceptibility/protective alleles. In addition to other variables, the results of the present article suggest that the major histocompatibility complex (MHC) class II profile may be of relevance for the study of a large-scale differentially expressed genes.

Several single nucleotide polymorphisms (SNPs) could indirectly, as well directly, influence metabolic parameters related to health effects in response to selenium (Se) supplementation. This study aimed to investigate whether the selenoprotein SNPs were associated with the response of Se status biomarkers to the Brazil nut consumption in patients using statins and if the variation in Se homoeostasis could affect antioxidant protection, lipid profile, muscle homoeostasis and selenoproteins mRNA. The study was performed in the Ribeirão Preto Medical School University Hospital. Thirty-two patients using statins received one unit of Brazil nut daily for 3 months. Body composition, blood Se concentrations, erythrocyte glutathione peroxidase (GPX) activity, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triacylglycerol (TAG), creatine kinase (CK) activity and gene expression of GPX1 and selenoprotein P (SELENOP) were evaluated before and after Brazil nut consumption. The volunteers were genotyped for SNP in GPX1 (rs1050450) and SELENOP (rs3877899 and rs7579). SNPs in selenoproteins were not associated with plasma and erythrocyte Se, but SNPs in SELENOP influenced the response of erythrocyte GPX activity and CK activity, TAG and LDL after Brazil nut consumption. Also, Brazil nut consumption increased GPX1 mRNA expression only in subjects with rs1050450 CC genotype. SELENOP mRNA expression was significantly lower in subjects with rs7579 GG genotype before and after the intervention. Thus, SNP in SELENOP could be associated with interindividual differences in Se homeostasis after Brazil nut consumption, emphasising the involvement of genetic variability in response to Se consumption towards health maintenance and disease prevention. [ABSTRACT FROM AUTHOR]

Regardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics. Results The use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR- 27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let- 7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR- 181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis. Conclusions These results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention. [ABSTRACT FROM AUTHOR]

Abstract: Type 1 diabetes mellitus (T1DM) results from an autoimmune attack against the insulin-producing pancreatic β-cells, leading to elimination of insulin production. The exact cause of this disorder is still unclear. Although the differential expression of microRNAs (miRNAs), small non-coding RNAs that control gene expression in a post-transcriptional manner, has been identified in many diseases, including T1DM, only scarce information exists concerning miRNA expression profile in T1DM. Thus, we employed the microarray technology to examine the miRNA expression profiles displayed by peripheral blood mononuclear cells (PBMCs) from T1DM patients compared with healthy subjects. Total RNA extracted from PBMCs from 11 T1DM patients and nine healthy subjects was hybridized onto Agilent human miRNA microarray slides (V3), 8x15K, and expression data were analyzed on R statistical environment. After applying the rank products statistical test, the receiver-operating characteristic (ROC) curves were generated and the areas under the ROC curves (AUC) were calculated. To examine the functions of the differentially expressed (p-value<0.01, percentage of false-positives <0.05) miRNAs that passed the AUC cutoff value ≥0.90, the database miRWalk was used to predict their potential targets, which were afterwards submitted to the functional annotation tool provided by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), version 6.7, using annotations from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We found 57 probes, corresponding to 44 different miRNAs (35 up-regulated and 9 down-regulated), that were differentially expressed in T1DM and passed the AUC threshold of 0.90. The hierarchical clustering analysis indicated the discriminatory power of those miRNAs, since they were able to clearly distinguish T1DM patients from healthy individuals. Target prediction indicated that 47 candidate genes for T1DM are potentially regulated by the differentially expressed miRNAs. After performing functional annotation analysis of the predicted targets, we observed 22 and 12 annotated KEGG pathways for the induced and repressed miRNAs, respectively. Interestingly, many pathways were enriched for the targets of both up- and down-regulated miRNAs and the majority of those pathways have been previously associated with T1DM, including many cancer-related pathways. In conclusion, our study indicated miRNAs that may be potential biomarkers of T1DM as well as provided new insights into the molecular mechanisms involved in this disorder. [Copyright &y& Elsevier]

Abstract: Patients with type 2 diabetes mellitus (T2DM) exhibit insulin resistance associated with obesity and inflammatory response, besides an increased level of oxidative DNA damage as a consequence of the hyperglycemic condition and the generation of reactive oxygen species (ROS). In order to provide information on the mechanisms involved in the pathophysiology of T2DM, we analyzed the transcriptional expression patterns exhibited by peripheral blood mononuclear cells (PBMCs) from patients with T2DM compared to non-diabetic subjects, by investigating several biological processes: inflammatory and immune responses, responses to oxidative stress and hypoxia, fatty acid processing, and DNA repair. PBMCs were obtained from 20 T2DM patients and eight non-diabetic subjects. Total RNA was hybridized to Agilent whole human genome 4×44K one-color oligo-microarray. Microarray data were analyzed using the GeneSpring GX 11.0 software (Agilent). We used BRB-ArrayTools software (gene set analysis — GSA) to investigate significant gene sets and the Genomica tool to study a possible influence of clinical features on gene expression profiles. We showed that PBMCs from T2DM patients presented significant changes in gene expression, exhibiting 1320 differentially expressed genes compared to the control group. A great number of genes were involved in biological processes implicated in the pathogenesis of T2DM. Among the genes with high fold-change values, the up-regulated ones were associated with fatty acid metabolism and protection against lipid-induced oxidative stress, while the down-regulated ones were implicated in the suppression of pro-inflammatory cytokines production and DNA repair. Moreover, we identified two significant signaling pathways: adipocytokine, related to insulin resistance; and ceramide, related to oxidative stress and induction of apoptosis. In addition, expression profiles were not influenced by patient features, such as age, gender, obesity, pre/post-menopause age, neuropathy, glycemia, and HbA1c percentage. Hence, by studying expression profiles of PBMCs, we provided quantitative and qualitative differences and similarities between T2DM patients and non-diabetic individuals, contributing with new perspectives for a better understanding of the disease. [Copyright &y& Elsevier]